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  "Description": "Implements a spatiotemporal boundary detection model with\na dissimilarity metric for areal data with inference in a\nBayesian setting using Markov chain Monte Carlo (MCMC). The\nresponse variable can be modeled as Gaussian (no nugget),\nprobit or Tobit link and spatial correlation is introduced at\neach time point through a conditional autoregressive (CAR)\nprior. Temporal correlation is introduced through a\nhierarchical structure and can be specified as exponential or\nfirst-order autoregressive. Full details of the package can be\nfound in the accompanying vignette. Furthermore, the details of\nthe package can be found in \"Diagnosing Glaucoma Progression\nwith Visual Field Data Using a Spatiotemporal Boundary\nDetection Method\", by Berchuck et al (2019)\n<doi:10.1080/01621459.2018.1537911>.",
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